The present invention, in some embodiments thereof, relates to medical imaging segmentation and, more particularly, but not exclusively, to segmentation of blood vessels and blood vessel networks in medical imaging applications.
Medical images are images of a human subject that are analyzed for the purposes of biological and medical research, diagnosing and treating disease, injury and birth defects. Commonly, medical images involve modalities that are designed for capturing data for imaging internal organs and tissues in a non-invasive manner. Examples of such modalities include computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), ultrasound, fluoroscopy, conventional x-rays, and the like. Medical images may be analogue or digital, two-dimensional or three-dimensional; however, three-dimensional modalities are digital.
When medical images are taken for diagnosis they are usually meticulously inspected by computer aided diagnosis (CAD) systems and/or trained medical practitioners, for example radiologists, to detect instances of abnormality that may be indicative of diseases. Additionally, the medical images may be used to accurately locate lesions so that treatments such as chemotherapy and radiotherapy may be precisely delivered and surgery may be effectively planned.
As medical images are usually three or four dimensional, the practitioner may step through a sequence of two-dimensional image slices at regular intervals, inspecting each slice. Thus, inspection of medical images may be tedious and prone to error. Accordingly, methods of computer aided detection (CAD) have been developed for the automatic location, registration, and segmentation. CAD may also be used for locating, characterizing, segmenting and diagnosing anatomical structures.
One of the tools that assist the practitioner is blood vessel segmentation. This tool may be used for facilitating diagnosis of vascular diseases, surgery planning, blood flow simulation, and the like. For example, published US patent application 2007/0031019, published in February 2008, describes a method for segmenting coronary vessels in digitized cardiac images that includes providing a digitized cardiac image, providing a seed point in the image, selecting a volume-of interest about the seed point, performing a local segmentation in the volume-of-interest, including initializing a connected component with the seed point and a threshold intensity value to the intensity of the seed point, adding a point to the connected component if the point is adjacent to the connected component and if the intensity of the point is greater than or equal to the threshold value, lowering the threshold intensity value, and computing an attribute value of the connected component, wherein if a discontinuity in the attribute value is detected, the local segmentation is terminated, wherein a local segmentation mask of a vessel is obtained.
A. Szymczaka, A. Stillman, A. Tannenbaum, and K. Mischaikow, “Coronary vessel tree from 3D imagery: A topological approach,” Medical Image Analysis Vol. 10(4), p. 548-559, 2006, describes a method of reconstructing vascular trees, including coronary trees, from three-dimensional medical images. Geometric filters are applied to remove spurious branches and fill in gaps in the vascular tree.
Published US patent application 2007/0165917, “Fully Automatic Vessel Tree Segmentation,” to Cao et al, describes an automatic vessel tree extraction module, that forms a vessel tree from a three-dimensional reconstructed image. The module computes characteristic paths for the vessels, discards some components and connects some components, to form the vessel tree.
Published US patent application 2008/0187199, “Robust Vessel Tree Modeling,” to Gulsun et al, describes a method of segmenting and modeling vascular structures from contrast enhanced cardiac CT and MRI images. A local centerline representation of vessels, including a full vessel tree, is constructed by a minimum-cost path detection algorithm, using a vesselness filter technique. In this algorithm, a seed voxel is chosen. To find a centerline path, a voxel is selected that has a largest distance to the seed voxel, and a path is found by backtracking until either the seed is reached, or a previously found path is reached.